A novel super-resolution urban green space segmentation network generating 0.2m resolution urban green space results using low-resolution imagery
Accurate mapping of urban green space (UGS) is crucial for urban planning and ecology. High-resolution drone images are often used for UGS identification, but their high cost limits large-scale mapping. To address this, we propose SR-UGSnet, a super-resolution segmentation framework that reconstruct...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2547928 |
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| Summary: | Accurate mapping of urban green space (UGS) is crucial for urban planning and ecology. High-resolution drone images are often used for UGS identification, but their high cost limits large-scale mapping. To address this, we propose SR-UGSnet, a super-resolution segmentation framework that reconstructs low-resolution remote sensing images using spatial redundancy from high-resolution data. The framework combines CNN and Transformer-based semantic segmentation to refine these images, improving segmentation from 0.8 m to 0.2 m resolution. SR-UGSnet shows robustness against mislabeled training data and achieves an overall accuracy (OA) of 83.21%-83.65% and IOU scores of 62.38%-64.23%. Our approach outperforms other greenfield mapping products, demonstrating the potential of using low-resolution imagery for high-resolution UGS segmentation. |
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| ISSN: | 1010-6049 1752-0762 |